Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge

Hongruixuan Chen; He Huang; Haifeng Wang; Jian Song; Junjue Wang; Weihao Xuan; Hamish Mitchell; Jiepan Li; Wei He; Liangpei Zhang; Zijie Wang; Chen Zhong; Jiazhen Zhao; Lei Hu; Ting Hu; Hongyan Zhang; Gregory Angelides; Miriam Cha; Clifford Broni-Bediako; Junshi Xia; Taylor Perron; Naoto Yokoya

arXiv · 2607.22746

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge

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Abstract

Rapid post-disaster response requires timely, building-level information on whether structures remain intact, are damaged, or are destroyed. Post-event optical imagery, however, may be unavailable because of cloud, smoke, or darkness. The Bright Challenge evaluated all-weather building damage mapping from a submeter-resolution pre-event optical image and a post-event SAR image. Participants were required to detect and delineate each building and assign exactly one of three mutually exclusive damage labels. The challenge extended the globally distributed \textsc{Bright} dataset with instance-level annotations for about 291,000 buildings across 16 disaster events spanning seven disaster types. The final phase was evaluated exclusively on two 2025 events absent from training: a wildfire event in California and a hurricane in Jamaica. A total of 157 participants made 1,289 submissions, and 46 teams entered the final phase. The two winning solutions achieved test mAPs of 0.182 and 0.181, approximately 8.7 times the public baseline of 0.021, but remained far below the best in-domain holdout score of 0.513. Across teams ranked in both phases, performance declined sharply and the rank order changed substantially. The two leading solutions independently favored modality-specific encoding, staged or late optical--SAR fusion, and an optical-dominant separation of building localization from damage recognition. The winning method additionally used scene-aware threshold adjustment and pseudo-label adaptation. These results identify cross-event generalization and stable severity discrimination as the principal remaining challenges. All data, annotations, baseline code, and winning solutions are publicly available at https://github.com/ChenHongruixuan/BRIGHT.

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Hongruixuan Chen, He Huang, Haifeng Wang, Jian Song, Junjue Wang, Weihao Xuan, Hamish Mitchell, Jiepan Li, Wei He, Liangpei Zhang, Zijie Wang, Chen Zhong, Jiazhen Zhao, Lei Hu, Ting Hu, Hongyan Zhang, Gregory Angelides, Miriam Cha, Clifford Broni-Bediako, Junshi Xia, Taylor Perron, Naoto Yokoya. 2026-07-23. Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge. https://arxiv.org/abs/2607.22746

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